无线电技术
医学
工作流程
领域(数学)
人工智能
步伐
深度学习
模式
数据科学
医学物理学
计算机科学
放射科
社会学
纯数学
地理
数据库
社会科学
数学
大地测量学
作者
Mathieu Hatt,Catherine Cheze Le Rest,Nils Antonorsi,Florent Tixier,Olena Tankyevych,Vincent Jaouen,François Lucia,Vincent Bourbonne,Ulrike Schick,Bogdan Badic,Dimitris Visvikis
标识
DOI:10.1053/j.semnuclmed.2020.09.002
摘要
This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all image modalities and clinical applications, however some of these are specific to PET/CT (and SPECT/CT) imaging and therefore the present paper focuses on those. In most cases, recommendations and potential methodological solutions do exist and should therefore be followed to improve the overall quality and reproducibility of published studies. In terms of future evolutions, the techniques from the larger field of artificial intelligence (AI), including those relying on deep neural networks (also known as deep learning) have already shown impressive potential to provide solutions, especially in terms of automation, but also to maybe fully replace the tools the radiomics community has been using until now in order to build the usual radiomics workflow. Some important challenges remain to be addressed before the full impact of AI may be realized but overall the field has made striking advances over the last few years and it is expected advances will continue at a rapid pace.
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